Model-based object classification and target recognition

ABSTRACT

Method for at least one of model-based classification and target recognition of an object. The method includes recording an image of an inanimate object, determining a feature that represents a part of the inanimate object, determining at least one condition associated with the feature that indicates an applicability of the feature based on at least one of geometry of the object, distance of the object from a camera, illumination conditions, contrast, speed of the object, height of the object, and relative position of the object to a camera, and carrying out the at least one of classification and target recognition of the object by recording the feature when the at least one condition indicates the applicability of the feature. At least one of object classification and target recognition is carried out for a feature of the object that is visible and recordable according to the position of the object. The recording, the determining the feature, the determining the at least one condition, and the carrying out are implemented on a computer.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to a model-based objectclassification and target recognition and in particular to a structureand the execution of models for object classification and localization.

2. Discussion of Background Information

All previously known methods from the prior art which use explicitgeometry models for matching extract only few features at the same timefrom the input data. There are several reasons for this.

For one reason, it is difficult to fuse different features so thatidentical benchmark values have an identical meaning. For anotherreason, there are purely practical reasons that will be explained inmore detail below.

Furthermore, the rules of when a feature of a model is to be checked,are either just as firmly programmed in as the feature itself or theyare determined from the geometry of the object.

The previously known systems, thus also those of D. G. Lowe in FittingParametrized Three-Dimensional Models to Images, IEEE Transact. onPattern Analysis and Machine Intelligence, Vol. 13. No. 5, 1991, thoseof L. Stephan et al. in Portable, scalable architecture for model-basedFLIR ATR and SAR/FLIR fusion, Proc. of SPIE, Vol. 0.3718, AutomaticTarget Recognition IX, August 1999 and those described in EP-A-622 750have in general a fixed arrangement of the image processing and inparticular a fixed arrangement of the preprocessing.

According to these known systems, the image is read in, then it ispreprocessed and subsequently matching is carried out. This means in theknown systems that either all preprocessing whose results are containedin any model has to be carried out or firmly implemented tests have tobe carried out that avoid this preprocessing.

A method for classifying documents, in particular bank notes, is knownfrom DE 10045360 A1 in which a document to be classified is classifiedin a certain class on the basis of features with higher significance. Inthis connection the document is subdivided into individual feature areaswhich are preferably square. Among these feature areas additionallyselected feature areas are formed which are used for determining theclass. The establishment of these selected feature areas thereby occursin a separate adaptation process before classification on the basis ofreference documents. In this connection the selected feature areas havea higher significance, i.e. deciding force, than the other featureareas.

SUMMARY OF THE INVENTION

One aspect of the present invention is therefore to make available amethod for object classification and target recognition which minimizesthe necessary computer resources and yet at the same time is morerobust.

Another aspect of the present invention is to make available a methodfor object classification and target recognition which minimizes thenumber of preprocessing steps.

These aspects and other aspects to be taken from the specification andfigures below are attained by a method for the model-basedclassification and/or target recognition of an object. The methodincludes recording an image of an object and determining a feature thatrepresents a part of the object. Moreover the method includesdetermining at least one condition that is linked to the feature andthat indicates the applicability of the feature and carrying out theclassification and/or target recognition of the object by recording thefeature if the condition indicates the applicability of the feature. Thedetermining a feature that represents a part of the object can furtherinclude the determination of a plurality of features, the determining atleast one condition can include the determination of at least onecondition for each of the features, and the carrying out theclassification can include the classification and/or target recognitionof the object through the detection of the plurality of features. Themethod can further include an algorithm for the at least one conditionwhich can be programmed freely as desired. Furthermore, the conditioncan be selected from one of geometry of the object, distance of theobject from a camera, illumination conditions, contrast, speed, of theobject, height of the object and relative position of the object to acamera. Moreover the method can include at least one step for thepreprocessing for the detection of a specific feature, and before thepreprocessing for the specific feature a test is carried out on whetherthe preprocessing for the specific feature has been carried out inconnection with another feature, and, if so, the use of thepreprocessing of the other feature for the specific feature.Additionally, the preprocessing carried out can be deposited in a cachememory. Moreover, the feature can be the “left edge” or “right edge” ofan object and each of these features can be included in the “edge image”preprocessing. Additionally, all reusable preprocessing steps can bestored in the sequence of compilation. Moreover, the cache may not berestricted in the type of preprocessing.

One aspect of the invention includes a method for at least one ofmodel-based classification and target recognition of an object. Themethod further includes recording an image of an object and determininga feature that represents a part of the object. Moreover, the methodincludes determining at least one condition associated with the featurethat indicates an applicability of the feature based on at least one of:geometry of the object, distance of the object from a camera,illumination conditions, contrast, speed of the object, height of theobject, and relative position of the object to a camera. Additionally,the method includes carrying out the at least one of classification andtarget recognition of the object by recording the feature when the atleast one condition indicates the applicability of the feature where theposition and orientation of the object are based upon at least one of animage-recording device, a technical device carrying the image-recordingdevice, objects classified and localized with the present method,objects classified or localized with other methods, and fixedfacilities.

In a further aspect of the invention, the method can include determiningof the feature that represents a part of the object comprisesdetermining a plurality of features. Moreover, the determining of the atleast one condition can include determining at least one condition foreach of the plurality of features, and the carrying out the at least oneclassification and target recognition of the object includes at leastone of classifying and target recognizing of the object through thedetection of the plurality of features. Furthermore, the determining ofthe feature that represents a part of the object can include determininga plurality of features. Additionally, the determining of at least onecondition can include determining at least one condition for each of theplurality of features. Moreover, the carrying out of the at least oneclassification and target recognition of the object can include at leastone of classifying and target recognizing of the object through thedetection of the plurality of features. The method can further include aprogrammable algorithm is associated with the at least one condition andthe method further can include programming the algorithm as desired.

Additionally, the method can include preprocessing for the detection ofa specific feature. Moreover, the method can include testing, before thepreprocessing for the detection of the specific feature, whether thepreprocessing for the detection of the specific feature has been carriedout in connection with another feature. Furthermore, the method caninclude using, when preprocessing for the detection of the specificfeature has been carried out for the another feature, the preprocessingof the another feature as the preprocessing for the detection of thespecific feature. Additionally, the method can include storing thepreprocessing in a cache memory. Moreover, the specific feature can beone of a left edge and right edge of an object and the preprocessing ofeach of these features comprises edge image preprocessing. Furthermore,the method can include storing all reusable preprocessing as a sequenceof compilation. Additionally, the cache may not restricted to a type ofpreprocessing.

Another aspect of the invention includes a method for at least one ofmodel-based classification and target recognition of an object. Themethod includes recording an image of an object and determining afeature that represents a part of the object. The method furtherincludes determining at least one condition associated with the featurethat indicates an applicability of the feature based on at least one of:geometry of the object, distance of the object from a camera,illumination conditions, contrast, speed of the object, height of theobject, and relative position of the object to a camera and carrying outthe at least one classification and target recognition of the object byrecording the feature when the condition indicates the applicability ofthe feature. Furthermore, the condition is one of geometry of theobject, distance of the object from a camera, illumination conditions,contrast, speed of the object, height of the object, and relativeposition of the object to the camera.

In a further aspect of the invention, the method can include determiningof the feature that represents a part of the object comprisesdetermining a plurality of features. Moreover, the determining of the atleast one condition can include determining at least one condition foreach of the plurality of features, and the carrying out the at least oneclassification and target recognition of the object includes at leastone of classifying and target recognizing of the object through thedetection of the plurality of features. Furthermore, the determining ofthe feature that represents a part of the object can include determininga plurality of features. Additionally, the determining of at least onecondition can include determining at least one condition for each of theplurality of features. Moreover, the carrying out of the at least oneclassification and target recognition of the object can include at leastone of classifying and target recognizing of the object through thedetection of the plurality of features. The method can further include aprogrammable algorithm is associated with the at least one condition andthe method further can include programming the algorithm as desired.Additionally, the method can include preprocessing for the detection ofa specific feature. Moreover, the method can include testing, before thepreprocessing for the detection of the specific feature, whether thepreprocessing for the detection of the specific feature has been carriedout in connection with another feature. Furthermore, the method caninclude using, when preprocessing for the detection of the specificfeature has been carried out for the another feature, the preprocessingof the another feature as the preprocessing for the detection of thespecific feature. Additionally, the method can include storing thepreprocessing in a cache memory. Moreover, the specific feature can beone of a left edge and right edge of an object and the preprocessing ofeach of these features comprises edge image preprocessing. Furthermore,the method can include storing all reusable preprocessing as a sequenceof compilation. Additionally, the cache may not restricted to a type ofpreprocessing.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will be explained in more detailon the basis of a drawing. They show:

FIG. 1 shows the sequence of operations of object recognition at thehighest level;

FIG. 2 shows The detailed sequence of operations of the matching blockof FIG 1;

FIG. 3 shows an image acquired in the image creation block of FIG. 1;

FIG. 4 shows a region (ROI) enclosing the sought objects, which regioncomprises a rectangular partial section of the image of FIG. 3; and

FIGS. 5 a, 5 b, 5 c, 5 d, and 5 e show how the feature request works onthe basis of the example of the edge receptor.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

The present invention is based on the knowledge that certain featuresare visible only from special views. Thus, e.g., the windows of thecargo hold doors of helicopters are visible only from the side, but notfrom other angles of view. This applies analogously to the illuminationconditions that permit the recognition of cargo hold doors or of otherelements of helicopters (such as, e.g., wheels, lifting load, etc.) onlyunder certain light conditions. Therefore, according to the presentinvention at least one feature to be recognized is linked to at leastone condition or at least one rule. Of course, it is possible to link aplurality of features to respective specific conditions and/or toassociate several conditions with a single feature to be recognized.Under these conditions only those features would thus have to beextracted from the image with which the respective linked condition ismet. In other words, no object classification and/or target recognitionneeds to be carried out for a cargo hold door that cannot be visible atall according to the position of the helicopter with reference to acamera.

According to the invention, the possibility was found of depositingvarious features (e.g., edges, area circumferences, hot spots) in themodel in a simple and consistent manner and of carrying out theextraction of these features in an effective manner.

If further features are to be extracted in the known image processingsystems according to the prior art cited above, their calls, includingparameter transfer, have to be explicitly programmed for eachapplication or each model. This can be more or less expensive, dependingon the system. This rigid sequence comprising the creation of an image,the segmentation of the created image and the preprocessing of the imagerecorded through the segmentation is known from EP-A-622 750.

In accordance with the present invention, each feature that is to berecognized is provided with a condition that establishes the condition'sapplicability. The algorithm of this condition can be freely programmedas desired and is not restricted only to the geometry of the object. Thecondition can also examine, e.g., the distance of the object to berecognized from the camera, the illumination conditions (e.g.,contrast), speed, height, relative position, etc.

By considering one or more of the conditions, the superfluous workcaused by “non-visibility” or “non-recordability” of a feature isavoided and the method according to the invention is at the same timemade more robust, since missing features do not lead to a worseassessment of the model.

According to a further particularly preferred aspect of the presentinvention, each feature that meets a condition and is thus required in apreprocessing of a partial step of the image processing, is requested bythis partial step. The sequence of the preprocessing as well as thealgorithm of the partial step are thereby deposited in the model (e.g.,as the number of a function in a list of available functions). Thesuperfluous work in a rigid arrangement of image creation, preprocessingand classification/localization, is thus avoided.

Since different partial steps may possibly need the same features (e.g.,the left edge and right edge features of an object require the “edgeimage” preprocessing) or partial results of lower preprocessingrepresent inputs for higher preprocessing (e.g., edge image and waveletsegmentation of the filtered original image, with the aid of which thelocal characteristics of a function can be studied efficiently by localwavelet bases), all reusable preprocessing steps are stored in thesequence of the compilation, beginning with the original image. If aspecific preprocessing is required, a “request” for this preprocessingwith all preceding steps of this preprocessing, beginning with theoriginal, is carried out through the image processing.

The treatment of the request lies in carrying out the preprocessing anddepositing and making available the result or, if already present,making available the deposited result, without carrying out a newcalculation. As already mentioned, existing preprocessing orpreprocessing series can thus be quickly called from an intermediatememory (cache). If, e.g., the preprocessing 1 is carried out for afeature A, and if preprocessing 1, 2 and 3 are necessary for a furtherfeature B, the preprocessing 1 of the feature 1 according to theinvention in intermediate storage can thus be accessed, which means theprocessing time is reduced.

With these steps it is possible to extract all the features necessaryfor the recognition of an object (after a corresponding normalization)and to feed them to the recognition process. One is therefore no longerrestricted to a small number of features for reasons of speed ormaintenance. Of course, the preprocessing of the system according to theinvention also takes time for calculation, but only calculations thatare absolutely necessary are carried out, since each preprocessing is tobe carried out only once. Different features can thus be extracted aslong as the total time of all preprocessing does not exceed the maximumrun time.

The method for preprocessing described above can be implementedaccording to the invention regardless of the fact that certain featuresare only visible from special views. In other words, the presentpreprocessing can be carried out independently of the link to one of thecertain conditions, although the combination of the two features has aparticularly advantageous effect with reference to the computerresources and the robustness of the system.

The method for preprocessing according to the invention is particularlyadvantageous compared to the prior art. The method presented by D. G.Lowe in Fitting Parametrized Three-Dimensional Models to Images, IEEETransact. on Pattern Analysis and Machine Intelligence, Vol. 13, No. 5,1991, recognizes the sought objects on the basis of edges. These edgesare expressed as parametrized curves and the free parameters (spatialposition and internal degrees of freedom) are determined through anapproximation method. The method is relevant in that it depositsgeometric preprocessing in a cache. However, the cache of the knownmethod of Lowe relates only to visibility conditions, whereas the cacheor intermediate memory according to the invention is not limited in thetype of preprocessing. Likewise the visibility conditions are determinedonly from the geometry of the object and are not freely selectable.Otherwise the method of Lowe is a typical representative of methods withfirmly implemented preprocessing.

The method according to L. Stephan et al. (Portable, scalablearchitecture for model-based FLIR ATR and SAR/FLIR fusion, Proc. ofSPIE, Vol. 3718, Automatic Target Recognition IX, August 1999) extractsfeatures not specified in detail from radar images (SAR) and extractsedges from the infrared images (FLIR images). A separate hypothesisformation is carried out with each of these features and finally thesehypotheses are fused. The entire preprocessing is implemented in a fixedsequence in the system; only the geometry models to be found areinterchangeable. The precise type and sequence of the preprocessing isgiven in EP-A-622 750.

A currently particularly preferred exemplary embodiment of the inventionwill now be explained with reference to the accompanying FIGS. 1 through5 e. This exemplary embodiment can be modified in a manner well known toone skilled in the art, and it is by no means intended to restrict thescope of protection of the invention to the example below. Rather thescope of protection is determined by the features of the claims andtheir equivalents.

FIG. 1 shows a sequence of operations of the object recognition at thehighest level. In step 1 acquiring the image with a camera, loading astored image or producing a VR image takes place in the image creationblock. An image acquired in the image creation block of FIG. 1 is shownby way of example in FIG. 3.

In step 2 (ROI creation) a simple and quick rough detection of theobject in the image takes place, i.e., a rectangular region that mostnearly encloses the sought objects is positioned. The abbreviation ROI(region of interest) denotes this region enclosing the sought objectswhich can be seen with reference to FIG. 4. Methods for determining suchan ROI are known per se. These include threshold value methods, pixelclassification, etc. An assignment of the currently formed ROI to an ROIfrom the last image must also be made.

In step 3 a decision is made on whether the object in the region ofinterest was provided with an ROI for the first time or not. This stepis necessary, since no hypotheses to be tested yet exist that areassigned to the ROI and so no test of the hypotheses can take place. Ifthe decision in step 3 is “yes,” the hypothesis initialization takesplace in step 4. Here the assignment of one or more 7-tuples to an ROIis carried out. The 7-tuple comprises the type of object (e.g., modelnumber (in the case of a helicopter I=Hind, 2=Helix, 3=Bell Ranger,etc.)) and the estimated six degrees of freedom under the assumption ofthis model class. The initial compilation of the six degrees of freedomcan be made, e.g., through systematic testing.

If the decision in step 3 is “no,” the hypotheses update is carried outin step 5. In the event of an already existing hypothesis, the newposition created by the movement of the object in space has to bematched to the position of the object in the image. To this end amovement prediction known in the prior art is carried out by means of atracker (e.g., Kalman filter).

The matching described in detail with reference to FIG. 2 takes place instep 16 of FIG. 1.

The 2D-3D pose estimate is implemented in step 6 of FIG. 1. The changeof position of the object in space can be estimated from the change ofposition of the receptors and the assumed position of the receptors inspace (from hypothesis) by means of the 2D-3D pose estimate. Methods forthis are known in the prior art (cf., e.g., Haralick: Pose Estimationfrom Corresponding Point Data, IEEE Transactions on Systems, Man andCybernetics, Vol. 19, No. 6, November/December 1989).

The quality of the model is determined in step 7 (“better” block) ofFIG. 1. This is necessary since the matching violates the rigidityproperty of the object. The rigidity is guaranteed through the poseestimation and new projection, since errors of individual receptors areaveraged and a single pose (6 degrees of freedom) is generated for allreceptors. A further matching in the same image is useful in order toachieve the best possible result here, i.e., the smallest possible errorbetween hypothesis and image. With a deterioration (or very slightimprovement), it is thereby assumed that the optimum point has alreadybeen reached.

The evaluation of all hypotheses, in particular their quality values, ofan ROI takes place in step 8 of FIG. 1 (“classification” block). Theclassification produces either the decision for a certain class and pose(by selection or combination of pose values of different hypotheses) orthe information that the object cannot be assigned to any known class.

The evaluation of class, quality and orientation takes place in step 9of FIG. 1. The information from the classification can be displayed tothe user in different ways (e.g., position and class as overlay in theimage) or actions can be directly derived therefrom (e.g., triggering aweapon). This can be determined after each image or at greater, regularintervals or when specific quality thresholds are exceeded or fallenbelow or the classification.

The details of the adjustment (matching) are explained with reference toFIG. 2.

The examination of rules takes place in step 10 of FIG. 2. The rule ofeach receptor is evaluated and incorporated into the 2D representation(graph) or not on the basis of the result of the receptor. Since variousrules can exist for various applications, which rules also process anydesired information to produce the rule result, how the method operatesis described here using the example of a geometrically motivated rulefunction. It should be noted that the parameters of the rule functionmust take into account not only the geometry of the object and itscurrent pose. Other information (e.g., position of the sun, horizonline, friend/foe positions, radio beacons, time of day), as available,can also contribute to the rule result.

The rule function of the vector angle rule contains three parametersthat are stored in the model:

-   -   a, b and x. Their result is r.    -   The rule function itself has the following form:

$\begin{matrix}{{\cos\;\beta} = \frac{\langle {{\underset{\underset{\_}{\_}}{R}\;\underset{\_}{x}},{- \underset{\_}{z}}} \rangle}{{{\underset{\underset{\_}{\_}}{R}\;\underset{\_}{x}}}{{- \underset{\_}{z}}}}} \\{r = \{ \begin{matrix}{1} & {\beta \prec a} & \; \\{1 - \frac{\beta - a}{b}} & {a \leq \beta \leq} & {a + b} \\{0} & {\beta \succ} & {a + b}\end{matrix} }\end{matrix}$

The vector z is the unit vector in direction z (view direction of thecamera). The matrix R is the rotation matrix from the hypothesis thatrotates the model from its original position (parallel to the cameracoordinates system) into its current view. The vector x is a vector thatdescribes the center view direction from the object outwards (e.g., theoutside normal of a surface).

If r produces a value different from 0, the receptor is incorporatedinto the 2D representation. The values between 0 and 1 are available forfurther evaluation but are not currently in use.

The projection of the receptors is carried out in step 11 of FIG. 2.

Step 11 is carried out separately (and possibly in a parallel manner)for each receptor that is included in the graph through the test. Thereceptor reference point p ³is thereby first projected into the imagematrix as p ².p ² =P ( R p ³ +t)

Matrix R is the above-mentioned rotation matrix, t is the vector fromthe beginning of the camera coordinate system to the beginning of themodel coordinate system in the scene (translation vector). Matrix P isthe projection matrix or camera model:

$\underset{\underset{\_}{\_}}{P} = \begin{bmatrix}{fs}_{x} & 0 & 0 \\0 & {fs}_{y} & 0 \\0 & 0 & 1\end{bmatrix}$

The value f is thereby the focal length of the camera, f_(sx) and f_(sy)the resolution of the camera in pixels e.g., per millimeter (mm). Thevalue p² is a homogenous vector (u, v and scaling) in pixels relative tothe camera perspective center. This is converted accordingly into thepixel coordinates x and y.

Subsequently the projection function of the receptor is called, whichfunction projects the receptor-specific data. An example of this is anedge receptor, the beginning and end points of which are defined in 3Don the model and are projected into the image matrix through thisfunction in the same way as the reference point.

The storage of the 3D points takes place in step 12. A list ofhypotheses points is created in 3D, whereby one or more points perreceptor are stored in a defined sequence. The receptor reference pointof each receptor can always be found in the list, further points areoptional. In addition the edge receptor stores the beginning and endpoints.

The graph creation is implemented in step 13. A graph is created throughtessellation from the mass of the points projected into the imagematrix, if it is necessary for the following matching process. Themethod used is known and described in the following article: Watson, D.F., 1981, Computing the n-dimensional Delaunay tessellation withapplication to Voronoi polytopes: The Computer J., 24(2), p. 167-172.

The 2D matching is carried out in step 14, whereby either the elasticgraph matching method according to Prof. v.d. Malsburg is carried out oranother method with similar objective. A method of this type wasimplemented by us that features special properties that are connected tothe tracking of the object. Through the method the best possibleposition of the sought feature has to be found near the start position,whereby a trade-off between feature quality and deviation from the givengraph configuration is desirable. In this step it is therefore necessaryto carry out some kind of scanning of the image with the applicationfunction of the receptor. The match quality of the application functionis assigned to each scanned position so that the most favorable positioncan be determined.

It will now be shown how the feature request works using the example ofthe edge receptor. To this end, the edge receptor algorithm is given thefollowing pseudocode:req=root of the preprocessing tree   (5.a)req=request(req.edge_image,threshold=10,sigma=1)  (5.b)req=request(req,distance_image,maximumdistance=100)   (5.c)image=image_fromtree(req)   (5.d)certain_chamfer_distance_along_the_line_(image, _line)   (5.e)

From the image creation (block 1) up to the beginning of 5 b, thepreprocessing cache is occupied only with the original image.

According to the pseudocode 5 a (see FIG. 5.a), the indicator req isplaced on the root of the tree.

In the request (5.b) (cf. FIG. 5 b) it is established that there are asyet no nodes of the edge image type with the above-mentioned parameters.This is then produced by the registered routine for calculating an edgeimage.

As shown in FIG. 5 c, (5.c) produces the distance image in the same way.

As shown in FIGS. 5 d and 5 e, (5.d) reads out the image from req and(5.e) calculates the quality of the feature in that at least one of reqand (5 e) determines the average distance (in pixels) from an imageedge. To this end the values are taken directly from the edge image.

In estimating the next position, the tree iterator (req) in (5.1) isre-placed at the root and in (5.b) and (5.c) it is moved on withoutcalculation.

Other receptors that are deposited in the model can expand this treefurther, as the free space on the right side of FIG. 5 e is intended toindicate.

The storage of the 2D points takes place in step 15 of FIG. 2. Thepoints p ² according to the matching step are deposited in a list in thesame sequences as in (12). It should thereby be ensured that thesynchronicity of both lists is still guaranteed in order to avoid anyinconsistencies in matching.

1. A method for at least one of model-based classification and targetrecognition of an object, the method comprising: recording an image ofan inanimate object; determining a feature that represents a recognizedpart of the inanimate object; determining at least one conditionassociated with the feature that indicates an applicability of thefeature based on at least one of: geometry of the object, distance ofthe object from a camera, illumination conditions, contrast, speed ofthe object, height of the object, and relative position of the object toa camera; carrying out the at least one of classification and targetrecognition of the object by recording the feature when the at least onecondition indicates the applicability of the feature; and matching aposition created by a movement of the inanimate object in space to theposition of the inanimate object in the image, wherein at least one ofobject classification and target recognition is carried out for afeature of the object that is visible and recordable according to theposition of the object, wherein the determining the at least onecondition occurs before the determining the feature, and wherein therecording, the determining the feature, the determining the at least onecondition, and the carrying out are implemented on a computer.
 2. Themethod according to claim 1, wherein the determining of the feature thatrepresents a part of the object comprises determining a plurality offeatures, wherein the determining of the at least one conditioncomprises determining at least one condition for each of the pluralityof features, and wherein the carrying out the at least oneclassification and target recognition of the object comprises at leastone of classifying and target recognizing of the object through thedetection of the plurality of features.
 3. The method according to claim1, wherein the determining of the feature that represents a part of theobject comprises determining a plurality of features.
 4. The methodaccording to claim 3, wherein the determining of at least one conditioncomprises determining at least one condition for each of the pluralityof features.
 5. The method according to claim 3, wherein the carryingout the at least one classification and target recognition of the objectcomprises at least one of classifying and target recognizing of theobject through the detection of the plurality of features.
 6. The methodaccording to claim 1, wherein a programmable algorithm is associatedwith the at least one condition and the method further comprisesprogramming the algorithm as desired.
 7. The method according to claim 1further comprising: preprocessing for the detection of a specificfeature; testing, before the preprocessing for the detection of thespecific feature, whether the preprocessing for the detection of thespecific feature has been carried out in connection with an otherfeature; and using, when preprocessing for the detection of the specificfeature has been carried out for the other feature, the preprocessing ofthe another feature as the preprocessing for the detection of thespecific feature.
 8. The method according to claim 7 further comprising:storing the preprocessing in a cache memory.
 9. The method according toclaim 7, wherein the specific feature is one of a left edge and rightedge of an object and the preprocessing of each of these featurescomprises edge image preprocessing.
 10. The method according to claim 7further comprising: storing all reusable preprocessing as a sequence ofcompilation.
 11. The method according to claim 8, wherein the cache isnot restricted to a type of preprocessing.
 12. The method according toclaim 1, wherein the at least one of object classification and targetrecognition is carried out for a feature of the object that is visibleand recordable according to the position of the object with reference toa camera.
 13. The method according to claim 1, wherein the object is anaircraft.
 14. The method according to claim 1, wherein the object is ahelicopter.
 15. The method according to claim 1, further comprising:detecting the inanimate object in the image using a region of interest(ROI); and matching a movement of the object in space to the position ofthe object in the image.
 16. The method according to claim 15, furthercomprising estimating a change in position of the object in space.
 17. Amethod for model-based classification and target recognition of anobject, the method comprising: recording an image of an inanimateobject; detecting the inanimate object in the image using a region ofinterest (ROI); determining a feature that represents a recognized partof the inanimate object; determining at least one condition associatedwith the feature that indicates an applicability of the feature based onat least one of: geometry of the object, distance of the object from acamera, illumination conditions, contrast, speed of the object, heightof the object, and relative position of the object to a camera; carryingout the at least one classification and target recognition of the objectby recording the feature when the condition indicates the applicabilityof the feature; extracting from the image only the feature which hasbeen linked to the at least one condition such that objectclassification need not be carried out for a feature of the object thatis not visible; and matching a position created by a movement of theinanimate object in space to the position of the inanimate object in theimage; wherein the recording, the determining the feature, thedetermining the at least one condition, and the carrying out areimplemented on a computer.
 18. The method according to claim 17, whereinthe determining of the feature that represents a part of the objectcomprises determining a plurality of features, wherein the determiningof the at least one condition comprises determining at least onecondition for each of the plurality of features, and wherein thecarrying out the at least one classification and target recognition ofthe object comprises at least one of classifying and target recognizingof the object through the detection of the plurality of features. 19.The method according to claim 17, wherein the determining of the featurethat represents a part of the object comprises determining a pluralityof features.
 20. The method according to claim 19, wherein thedetermining of at least one condition comprises determining at least onecondition for each of the plurality of features.
 21. The methodaccording to claim 19, wherein the carrying out of the at least oneclassification and target recognition of the object comprises at leastone of classifying and target recognizing of the object through thedetection of the plurality of features.
 22. The method according toclaim 17, wherein a programmable algorithm is associated with the atleast one condition and the method further comprises programming thealgorithm as desired.
 23. The method according to claim 17 furthercomprising: preprocessing for the detection of a specific feature;testing, before the preprocessing for the detection of the specificfeature, whether the preprocessing for the detection of the specificfeature has been carried out in connection with an other feature; andusing, when preprocessing for the detection of the specific feature hasbeen carried out for the other feature, the preprocessing of the anotherfeature as the preprocessing for the detection of the specific feature.24. The method according to claim 23 further comprising: storing thepreprocessing in a cache memory.
 25. The method according to claim 23,wherein the specific feature is one of a left edge and right edge of anobject and the preprocessing of each of these features comprises edgeimage preprocessing.
 26. The method according to claim 23 furthercomprising: storing all reusable preprocessing as a sequence ofcompilation.
 27. The method according to claim 24, wherein the cache isnot restricted to a type of preprocessing.
 28. The method according toclaim 17, wherein the object is an aircraft.
 29. The method according toclaim 17, wherein the object is a helicopter.
 30. The method accordingto claim 17, further comprising: estimating a change in position of theobject in space.
 31. A method for model-based classification and targetrecognition of an object, the method comprising: recording an image ofan inanimate object; detecting the inanimate object in the image bycreating a region of interest (ROI); determining a feature thatrepresents a recognized part of the inanimate object; determining atleast one condition linked with the feature that indicates anapplicability of the feature; and carrying out object classification andtarget recognition of the object when the at least one conditionindicates the applicability of the feature and when the feature of theobject is visible and recordable according to the position of the objectwith reference to a camera; matching a position created by a movement ofthe inanimate object in space to the position of the inanimate object inthe image, wherein the at least one condition comprises one of geometryof the object, distance of the object from a camera, illuminationconditions, contrast, speed of the object, height of the object, andrelative position of the object to a camera, and wherein the recording,determining the feature and the at least one condition and the carryingout are implemented on a computer.
 32. The method according to claim 31,wherein the object is a helicopter.